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                        Jupyter-notebook拓展应用
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                            <b>2.4.</b>
                        
                        包和环境管理器：conda
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                        pip和Virtualenv
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                        标记语言：Markdown
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                        Markdown语法
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                        文档管理工具-Gitbook
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                            <b>3.</b>
                        
                        数据分析库-Pandas
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                            <b>3.1.</b>
                        
                        pandas
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                            <b>3.2.</b>
                        
                        Series
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                            <b>3.3.</b>
                        
                        DataFrame对象-创建
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                        DataFrame查询1-整体
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                            <b>4.2.</b>
                        
                        DataFrame查询2-专用查询
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                        DataFrame查询3-专有查询：过滤查询
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                            <b>4.4.</b>
                        
                        Pandas对象的数据操作：增删改查
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                            <b>4.4.1.</b>
                        
                        Pandas数据操作：其他操作
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                            <b>4.4.2.</b>
                        
                        Pandas数据存取
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                            <b>4.4.3.</b>
                        
                        Pandas数据运算
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                            <b>4.4.3.1.</b>
                        
                        Pandas数据运算-拓展
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                            <b>4.4.4.</b>
                        
                        Pandas分组聚合1
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                            <b>4.4.5.</b>
                        
                        Pandas分组聚合2
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                            <b>4.4.6.</b>
                        
                        Pandas数据规整-清理
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                            <b>4.4.7.</b>
                        
                        Pandas数据规整-转换
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                            <b>4.4.7.1.</b>
                        
                        离散化和面元划分
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                            <b>4.4.8.</b>
                        
                        Pandas数据规整-合并
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                            <b>4.5.</b>
                        
                        Pandas数据规整-重塑和轴向旋转
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                            <b>4.5.1.</b>
                        
                        透视表和交叉表
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                            <b>5.</b>
                        
                        Python可视化
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                            <b>5.1.</b>
                        
                        基础：Matplotlib常见图表
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                            <b>5.1.1.</b>
                        
                        Matplotlib常见设置和操作
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                            <b>5.2.</b>
                        
                        提升：绘图区域
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                            <b>5.3.</b>
                        
                        提升：绘图组件
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                            <b>5.4.</b>
                        
                        拓展：高级绘图
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                            <b>5.5.</b>
                        
                        拓展：数学计算展示图像
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                            <b>5.6.</b>
                        
                        拓展：注意事项
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                            <b>5.7.</b>
                        
                        拓展：pylab
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                            <b>6.</b>
                        
                        数据分析必备知识点
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                            <b>7.</b>
                        
                        数据分析流程
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<h1 id="&#x6570;&#x5B66;&#x8BA1;&#x7B97;&#x5C55;&#x793A;&#x56FE;&#x50CF;">&#x6570;&#x5B66;&#x8BA1;&#x7B97;&#x5C55;&#x793A;&#x56FE;&#x50CF;</h1>
<p>&#x7ED8;&#x5236;&#x4E00;&#x4E2A;&#x7B80;&#x5355;&#x7EBF;&#x6027;&#x51FD;&#x6570;&#x56FE;&#x50CF;</p>
<pre><code class="lang-python"><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">import</span> matplotlib.pyplot <span class="hljs-keyword">as</span> plt
<span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd
plt.rcParams[<span class="hljs-string">&apos;font.family&apos;</span>] = [<span class="hljs-string">&apos;Arial Unicode MS&apos;</span>, <span class="hljs-string">&apos;Microsoft Yahei&apos;</span>, <span class="hljs-string">&apos;SimHei&apos;</span>, <span class="hljs-string">&apos;sans-serif&apos;</span>]
</code></pre>
<pre><code class="lang-python"><span class="hljs-comment">#&#x6570;&#x636E;</span>
x = np.array([-<span class="hljs-number">15</span>, -<span class="hljs-number">5</span>, <span class="hljs-number">0</span>, <span class="hljs-number">5</span>, <span class="hljs-number">8</span>, <span class="hljs-number">10</span>])
<span class="hljs-comment"># x = [-15, -5, 0, 5, 8, 10]</span>
a = <span class="hljs-number">2</span>
b = <span class="hljs-number">5</span>

<span class="hljs-comment"># &#x7EBF;&#x6027;&#x51FD;&#x6570; y=ax+b</span>
<span class="hljs-comment"># y = []</span>
<span class="hljs-comment"># for i in x:</span>
<span class="hljs-comment">#     y.append(a * i + b )</span>



y = a * x + b
y



<span class="hljs-comment"># &#x7ED8;&#x5236;</span>
plt.plot(x,y)
<span class="hljs-comment"># plt.show()</span>
</code></pre>
<pre><code>[&lt;matplotlib.lines.Line2D at 0x4fb39b0&gt;]
</code></pre><p><img src="images/output_2_1.png" alt="png"></p>
<pre><code class="lang-python"><span class="hljs-comment"># &#x7ED8;&#x5236;</span>
plt.figure(figsize=(<span class="hljs-number">10</span>,<span class="hljs-number">6</span>),dpi=<span class="hljs-number">200</span>)

plt.plot(x,y)

<span class="hljs-comment">#&#x7EC4;&#x4EF6;</span>

<span class="hljs-comment"># &#x6807;&#x9898;&#xFF0C;&#x53EF;&#x4EE5;&#x4F7F;&#x7528;$&#x5F15;&#x5165;&#x90E8;&#x5206;latex&#x6587;&#x672C;&#x6392;&#x7248;&#x8BED;&#x6CD5;&#x548C;&#x516C;&#x5F0F;</span>
plt.title(<span class="hljs-string">&apos;&#x7EBF;&#x6027; $y=ax+b$&#xFF0C;&#x975E;&#x7EBF;&#x6027; $y=ax^{2}+bx+c$&apos;</span>) 

<span class="hljs-comment"># &#x5750;&#x6807;&#x8F74;&#x6807;&#x6CE8;&#xFF0C;&#x5F53;&#x5B57;&#x7B26;&#x4E32;&#x91CC;&#x6709;&#x53CD;&#x659C;&#x6760;&#x7B49;&#x8F6C;&#x4E49;&#x7279;&#x6B8A;&#x5B57;&#x7B26;&#x65F6;,&#x5B57;&#x7B26;&#x4E32;&#x524D;&#x52A0;`r`,&#x8868;&#x793A;&#x663E;&#x793A;&#x539F;&#x59CB;&#x5B57;&#x7B26;&#x4E32;</span>
plt.xlabel(<span class="hljs-string">r&apos;X \axis&apos;</span>) 

<span class="hljs-comment">###############</span>

<span class="hljs-comment"># &#x64CD;&#x4F5C;&#x8F74;&#x7EBF;</span>

<span class="hljs-comment"># gca&#xFF0C;get corrent axis&#xFF0C;&#x83B7;&#x53D6;&#x8F74;</span>
ax = plt.gca()

<span class="hljs-comment">#&#x4EA4;&#x6362;&#x8F74;&#x7EBF;</span>
<span class="hljs-comment"># ax.xaxis.set_ticks_position(&apos;top&apos;)</span>
<span class="hljs-comment"># ax.yaxis.set_ticks_position(&apos;right&apos;)</span>

<span class="hljs-comment">#&#x53BB;&#x9664;&#x4E0A;&#x4FA7;&#x548C;&#x53F3;&#x4FA7;&#x7684;&#x7A7A;&#x767D;&#x8F74;&#x7EBF;</span>
ax.spines[<span class="hljs-string">&apos;right&apos;</span>].set_color(<span class="hljs-string">&apos;none&apos;</span>)
ax.spines[<span class="hljs-string">&apos;top&apos;</span>].set_color(<span class="hljs-string">&apos;none&apos;</span>)

<span class="hljs-comment">#&#x79FB;&#x4F4D;&#x8F74;&#x7EBF;</span>
ax.spines[<span class="hljs-string">&apos;bottom&apos;</span>].set_position([<span class="hljs-string">&apos;data&apos;</span>, -<span class="hljs-number">10</span>])
ax.spines[<span class="hljs-string">&apos;left&apos;</span>].set_position([<span class="hljs-string">&apos;data&apos;</span>, <span class="hljs-number">0</span>])

<span class="hljs-comment">###########</span>

<span class="hljs-comment">#&#x6DFB;&#x52A0;&#x6CE8;&#x89E3;&#x6807;&#x8BB0;</span>

<span class="hljs-comment">#&#x7F16;&#x6807;&#x8BB0;&#x70B9;</span>

x0 = -<span class="hljs-number">2.5</span>
y0 = a * x0 + b
plt.scatter(x0,y0,color=<span class="hljs-string">&apos;red&apos;</span>)

<span class="hljs-comment">#&#x6807;&#x8BB0;&#x7EBF;</span>
plt.plot(
    [x0,x0],
    [y0,-<span class="hljs-number">10</span>],
    linestyle = <span class="hljs-string">&apos;--&apos;</span>,
    color = <span class="hljs-string">&apos;yellow&apos;</span>,
)

<span class="hljs-comment"># &#x4EFB;&#x610F;&#x4F4D;&#x7F6E;&#x589E;&#x52A0;&#x6587;&#x672C;</span>
plt.text(
    <span class="hljs-number">5</span>,  <span class="hljs-comment">#x&#x8F74; &#x5750;&#x6807;</span>
    <span class="hljs-number">20</span>,  <span class="hljs-comment">#y &#x8F74;&#x5750;&#x6807;</span>
    <span class="hljs-string">&apos;&#x6211;&#x662F;&#x56FE;&#x7247;&#x5185;&#x6587;&#x672C;&#xFF01;&apos;</span>,
    fontsize=<span class="hljs-number">18</span>,
    rotation=<span class="hljs-number">30</span>,

)
<span class="hljs-comment"># &#x4EFB;&#x610F;&#x4F4D;&#x7F6E;&#x589E;&#x52A0;&#x5E26;&#x7BAD;&#x5934;&#x7684;&#x6CE8;&#x91CA;&#x6587;&#x672C;</span>
plt.annotate(
    <span class="hljs-string">r&apos;$Linear\ function\ y=ax+b$&apos;</span>,  <span class="hljs-comment">#&#x663E;&#x793A;&#x5B57;&#x7B26;&#x4E32;&#xFF0C;&#x7A7A;&#x683C;&#x5728;$&#x5185;&#x4E0D;&#x663E;&#x793A;&#xFF0C;&#x7528;&#x53CD;&#x659C;&#x6760;&#x8F6C;&#x4E49;</span>
    xytext=(-<span class="hljs-number">15</span>, <span class="hljs-number">15</span>),  <span class="hljs-comment"># &#x6587;&#x672C;&#x4F4D;&#x7F6E;</span>
    xy=(x0, y0),  <span class="hljs-comment"># &#x7BAD;&#x5934;&#x6307;&#x5411;&#x4F4D;&#x7F6E;</span>

    arrowprops=dict(  <span class="hljs-comment"># &#x5B57;&#x5178;&#x7C7B;&#x578B;&#xFF0C;&#x5B9A;&#x4E49;&#x7BAD;&#x5934;&#x6837;&#x5F0F;</span>
        arrowstyle = <span class="hljs-string">&apos;fancy&apos;</span>,  <span class="hljs-comment"># &#x7BAD;&#x5934;&#x6837;&#x5F0F;&#xFF0C;&#x4F8B;&#x5982; -&gt;,&lt;-,|-|,simple,fancy</span>
        color = <span class="hljs-string">&apos;green&apos;</span>,  <span class="hljs-comment"># &#x7BAD;&#x5934;&#x989C;&#x8272;</span>
        connectionstyle=<span class="hljs-string">&quot;arc3,rad=.5&quot;</span>,  <span class="hljs-comment"># &#x7BAD;&#x5934;&#x5F27;&#x5EA6;</span>
    ),
    <span class="hljs-comment"># &#x4E0B;&#x9762;&#x4E24;&#x4E2A;&#x53C2;&#x6570;&#x5C06;&#x7EDD;&#x5BF9;&#x5750;&#x6807;&#x4F4D;&#x7F6E;&#x8BBE;&#x4E3A;&#x76F8;&#x5BF9;&#x5750;&#x6807;</span>

<span class="hljs-comment">#     xycoords = &apos;data&apos;,  # &#x76F8;&#x5BF9;&#x9ED8;&#x8BA4;&#x5750;&#x6807;&#x7CFB;&#x504F;&#x79FB;   </span>
<span class="hljs-comment">#     textcoords = &apos;offset points&apos;,  # &#x76F8;&#x5BF9;&#x5750;&#x6807;&#xFF0C;&#x76F8;&#x5BF9;&#x7BAD;&#x5934;&#x539F;&#x70B9;&#x504F;&#x79FB;</span>

)
plt.show()
</code></pre>
<p><img src="images/output_3_0.png" alt="png"></p>
<hr>
<p>&#x4F7F;&#x7528; Pycharm &#x6216;&#x5176;&#x4ED6;&#x7F16;&#x8F91;&#x5668;&#x7ED8;&#x56FE;</p>
<hr>
<p>pylab&#xFF0C;&#x81EA;&#x52A8;&#x8F7D;&#x5165; numpy &#x548C; Matplotlib</p>
<pre><code class="lang-python">%pylab inline
</code></pre>
<pre><code>Populating the interactive namespace from numpy and matplotlib
</code></pre><pre><code class="lang-python">plot([<span class="hljs-number">1</span>,<span class="hljs-number">2</span>,<span class="hljs-number">3</span>,<span class="hljs-number">4</span>])
</code></pre>
<pre><code>[&lt;matplotlib.lines.Line2D at 0x7ab6320&gt;]
</code></pre><p><img src="images/output_6_1.png" alt="png"></p>

                    
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